{"id":"W4292692863","doi":"10.21154/elbarka.v4i1.3016","title":"Forecasting of Indonesia's Gross Domestic Product Amid Covid-19 Pandemic","year":2021,"lang":"en","type":"article","venue":"El-Barka Journal of Islamic Economics and Business","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Autoregressive integrated moving average; Christian ministry; Coronavirus disease 2019 (COVID-19); Pandemic; Gross domestic product; Geography; Indonesian; Business; Economic growth; Economics; Political science; Statistics; Mathematics; Time series; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000624141,0.0004016437,0.0002865004,0.0006668122,0.0001667823,0.0008288931,0.0003182079,0.0004788616,0.0008112743],"category_scores_gemma":[0.001666118,0.0001536156,0.0003337071,0.0006297106,0.0001716869,0.0006466437,0.0003176411,0.0007291503,0.0003744697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008691189,"about_ca_system_score_gemma":0.0006785842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.025659,"about_ca_topic_score_gemma":0.01386499,"domain_scores_codex":[0.999813,0.00003876083,0.00001695006,0.00004684009,0.00005309141,0.00003145658],"domain_scores_gemma":[0.9993687,0.0002225267,0.0001155074,0.0000276727,0.0002032606,0.00006225408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002816988,0.0001957902,0.4993379,0.0001825572,0.00009165345,0.001321474,0.0004029235,0.4340766,0.002558251,0.004001195,0.01457331,0.04297666],"study_design_scores_gemma":[0.00001003012,0.00006993293,0.1493401,0.00003335726,0.00001911375,0.00009622474,0.0004800007,0.8449951,0.001211522,0.001113583,0.002599734,0.0000312111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798769,0.0002752639,0.006293037,0.0009948036,0.0001028747,0.00004451588,0.004364144,0.0001910592,0.007857296],"genre_scores_gemma":[0.9941682,0.0002408133,0.002245837,0.00002919411,0.00001941704,0.00001989042,0.002137967,0.00001032696,0.001128346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.025659,"threshold_uncertainty_score":0.05101931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03030405680275415,"score_gpt":0.2809412956502209,"score_spread":0.2506372388474667,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}